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Predicting groundwater level changes within the irrigation network range using the tree algorithm (case study: Alborz plain)

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Abstract

Groundwater drawdown is a damaging and destructive component in agriculture, demonstrating the necessity for a pattern to assist managers and farmers in predicting the amount of water decrease for proper groundwater planning and management. Because a variety of variables influence the quantity of groundwater drawn down, the current study focuses on human and environmental factors that are useful in forecasting the amount of groundwater levels change within the irrigation network range in the Alborz plain region from 2004 to 2018. Four tree algorithms were employed to forecast changes in groundwater levels. The outcomes of four algorithms in forecasting groundwater level change were evaluated: C5.0 and Classification and Regression Tree (CART), Chi-square Automatic Interaction Detector (CHAID), Quick, Unbiased, Efficient, and Statistical Tree (QUEST). The results for various indices reveal that the performance of the C5.0 algorithm is superior to that of the other methods. The findings of the C5.0 algorithm demonstrate that the volume of agricultural water demand, air humidity, and the amount of water provided to the irrigation network are the three most critical elements determining groundwater level fluctuations. As a result, the proposed method can estimate the amount of change in groundwater levels, which may aid in improved groundwater management and reduce the negative consequences of groundwater drawdown.

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Abbreviations

AH:

Air humidity (%)

AT:

Air temperature (°C)

AW:

The volume of water discharged from agricultural wells (million cubic meters)

ER:

Error rate

ET:

Potential evapotranspiration (millimetre per day)

PPV:

Positive predictive value or precision

TNR:

True negative rate

TPR:

The true positive rate

VP:

The volume of precipitation (million cubic meters)

WD:

The volume of agricultural water demand (million cubic meters)

WI:

The volume of water delivered to irrigation network (million cubic meters)

References

  • Aertsen W, Kint V, Orshoven JV, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221(8):1119–1130

    Article  Google Scholar 

  • Afshar FA, Ayoubi S, Besalatpour AA, Khademi H, Castrignano A (2016) Integrating auxiliary data and geophysical techniques for the estimation of soil clay content using CHAID algorithm. J Appl Geophys 126:87–97

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D et al (1998) FAO irrigation and drainage paper no. 56. Food and Agriculture Organization of the United Nations, Rome, vol 56(97), pp 24–25

  • Bichler A, Neumaier A, Hofmann T (2014) A tree-based statistical classification algorithm (CHAID) for identifying variables responsible for the occurrence of faecal indicator bacteria during waterworks operations. J Hydrol 519:909–917

    Article  Google Scholar 

  • Bonsor H, MacDonald A, Ahmed K, Burgess W, Basharat M, Calow R, Dixit A, Foster S, Gopal K, Lapworth D et al (2017) Hydrogeological typologies of the indo-gangetic basin alluvial aquifer, south asia. Hydrogeol J 25(5):1377–1406

    Article  CAS  Google Scholar 

  • Bozkir AS, Sezer EA (2011) Predicting food demand in food courts by decision tree approaches. Proc Comp Sci 3:759–763

    Article  Google Scholar 

  • Chou JS (2012) Comparison of multilabel classification models to forecast project dispute resolutions. Expert Syst Appl 39(11):10202–10211

    Article  Google Scholar 

  • Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inf 35:352–359. https://doi.org/10.1016/S1532-0464(03)00034-0

    Article  Google Scholar 

  • Han J, Kamber M, Pei J (2011) Data mining concepts and techniques, 3rd edn. Morgan Kaufmann, California, p 703

    Google Scholar 

  • Hassani Y, Hashemy Shahdany SM (2019) Agricultural water distribution under drought conditions based on economic priorities: case study of Qazvin Irrigation District. Irrig Drain 68(3):443–451

    Article  Google Scholar 

  • Khashei-Siuki A, Sharifan H (2020) Comparison of AHP and FAHP methods in determining suitable areas for drinking water harvesting in Birjand aquifer. Iran Groundw Sustain Dev 10:100328

    Article  Google Scholar 

  • Konapala G, Mishra AK, Wada Y, Mann ME (2020) Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat Commun 11(1):1–10

    Article  Google Scholar 

  • Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39(4):261–283

    Article  Google Scholar 

  • Lee S, Lee CW (2015) Application of decision-tress model to groundwater productivity-potential mapping. Sustainability 7(10):13416–13432

    Article  Google Scholar 

  • Loh WY, Shih YS (1997) Split selection methods for classification trees. Stat Sin 7:815–840

    Google Scholar 

  • Mirhashemi SH, Mirzaei F (2021) Extracting association rules from changes in aquifer drawdown in irrigation areas of Qazvin plain, Iran. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2020.100495

    Article  Google Scholar 

  • Mirhashemi SH, Haghighat Jou P, Mirzaei F, Panahi M (2020) The study of environmental and human factors affecting aquifer depth changes using tree algorithm. Int J Environ Sci Technol 17(3):1825–1834

    Article  Google Scholar 

  • Naderi MM, Mirchi A, Bavani ARM, Goharian E, Madani K (2021) System dynamics simulation of regional water supply and demand using a food-energy-water nexus approach: application to Qazvin Plain. Iran J Environ Manage 280:111843

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(1):1–27

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap M (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  • Novinpour EA (2017) A study of the relationship between the exploitation and subsidence of Salmas. Urban Manage 15(45):319–326

    Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  • Srivastava A, Han EH, Kumar V, Singh V (1999) Parallel formulations of decision-tree classification algorithms. High performance data mining. Springer, Boston, MA, pp 237–261

    Google Scholar 

  • Stumpp C, Żurek AJ, Wachniew P, Gargini A, Gemitzi A, Filippini M, Witczak SA (2016) decision tree tool supporting the assessment of groundwater vulnerability. Environ Earth Sci 75(13):1057

    Article  Google Scholar 

  • Sut N, Simsek O (2011) Comparison of regression tree data mining methods for prediction of mortality in head injury. Expert Syst Appl 38(12):15534–15539

    Article  Google Scholar 

  • Wu X, Kumar V, Ross-Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2009) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank the Coordinatorship of the Scientific Research Projects of University Zanjan and Zabol

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran

    S. H. Mirhashemi

  2. Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

    M. Panahi

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  1. S. H. Mirhashemi
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  2. M. Panahi
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Correspondence to M. Panahi.

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Editorial responsibility: M. Abbaspour.

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Mirhashemi, S.H., Panahi, M. Predicting groundwater level changes within the irrigation network range using the tree algorithm (case study: Alborz plain). Int. J. Environ. Sci. Technol. 19, 9817–9826 (2022). https://doi.org/10.1007/s13762-022-04176-x

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  • DOI: https://doi.org/10.1007/s13762-022-04176-x

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